Artificial Intelligence Business Technology

Your AI Survival Guide: Scraped Knees, Bruised Elbows, and Lessons Learned from Real-World AI Deployments (Summary)

by Sol Rashidi

Imagine spending millions on a state-of-the-art innovation, only to join the 87% of companies whose AI projects never actually make it into production. These projects don't fail because the algorithm is wrong; they die in 'pilot purgatory' because the business forgot the most crucial, non-technical ingredient: a clear, valuable problem to solve.

Don't Start with AI, Start with a Problem

The most common reason AI projects fail is that companies become infatuated with the technology itself, rather than identifying a specific, high-value business problem that AI is uniquely suited to solve. Technology in search of a problem rarely succeeds.

A company might spend a year and millions of dollars developing a sophisticated AI chatbot for customer service, only to discover that 90% of customer queries could have been solved more quickly and cheaply by a well-designed FAQ page. The real problem wasn't a lack of AI, but a poorly organized help section.

Your AI is Only as Good as Your Data Janitors

The glamorous part of AI is the algorithm, but an estimated 80% of the work is the unglamorous, manual effort of cleaning, labeling, and preparing data. Neglecting this 'data plumbing' is the primary reason models deliver inaccurate or useless results.

An insurance company tried to build an AI to predict fraudulent claims. The model failed because its historical data was a mess: claim forms had inconsistent fields, crucial data was missing, and different departments used different codes for the same thing. The AI couldn't learn from chaos, rendering the project useless until a massive data-cleaning effort was undertaken.

AI is a C-Suite Sport, Not an IT Project

Successful AI implementation requires buy-in and active participation from across the entire organization, from the CEO down. When AI is siloed in the IT or data science department, it fails because it lacks the business context and cross-functional support needed to integrate into actual workflows.

A retail company's data science team built a technically perfect AI model to optimize inventory. It failed in practice because the supply chain team wasn't consulted on logistics constraints, the marketing team wasn't looped in on upcoming promotions, and store managers weren't trained on how to use the new recommendations. The project was a technical success but an organizational failure.

Measure 'Use Case Velocity,' Not Just ROI

Instead of getting bogged down trying to prove the ROI of a single, massive AI project, companies should focus on rapidly identifying, testing, and deploying (or killing) many small-scale AI use cases. This builds momentum, organizational learning, and a portfolio of quick wins.

Instead of spending two years building an all-encompassing fraud detection system, a bank could launch a small-scale AI tool in three months that only flags suspicious transactions for one type of credit card. This quick win builds confidence, provides immediate value, and generates crucial learnings for the next, more ambitious project.

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